A Review on Fault Detection and Process Diagnostics in Industrial Processes

Park, You‐Jin; Fan, Shu-Kai S.; Hsu, Chia‐Yu

doi:10.3390/pr8091123

Cited by 162 publications

(75 citation statements)

References 154 publications

(264 reference statements)

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“…The probability of failure and fault in chemical system also increases. Compared with the permanent termination of the system caused by failure, the system offset caused by fault can be detected in time, and then corresponding measures can be taken to avoid accidents [ 1 ]. Therefore, distinguishing the type of faults in the chemical process is the key to reducing operator errors and ensuring system safety and reliability.…”

Section: Introductionmentioning

confidence: 99%

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

Zhang

Luo

Dai

2021

Sensors

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This study considers the problem of distinguishing between process and sensor faults in nonlinear chemical processes. An integrated fault diagnosis framework is proposed to distinguish chemical process sensor faults from process faults. The key idea of the framework is to embed the cycle temporal algorithm into the dynamic kernel principal component analysis to improve the fault detection speed and accuracy. It is combined with the fault diagnosis method based on the reconstruction-based contribution graph to diagnose the fault variables and then distinguish the two fault types according to their characteristics. Finally, the integrated fault diagnosis framework is applied to the Tennessee Eastman process and acid gas absorption process, and its effectiveness is proved.

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Section: Introductionmentioning

confidence: 99%

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

Zhang

Luo

Dai

2021

Sensors

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“…[1]. At present, neural network models have made remarkable achievements in the fields of image recognition,fault detection and classification (FDC) [2][3][4], natural language processing, and so on. In practice, the hyper-parameters of models rely on experience and a large number of attempts is not only time-consuming and computationally expensive for algorithm training but also does not always maximize the performance of the model [5,6].…”

Section: Introductionmentioning

confidence: 99%

A New Hyper-Parameter Optimization Method for Power Load Forecast Based on Recurrent Neural Networks

Zhang

Cai

2021

Algorithms

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The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experiences. In recent years, multiple algorithms based on Bayesian optimization (BO) are developed to determine the optimal values of the hyper-parameters. In most of these methods, gradients are required to be calculated. In this work, the particle swarm optimization (PSO) is used under the BO framework to develop a new method for hyper-parameter optimization. The proposed algorithm (BO-PSO) is free of gradient calculation and the particles can be optimized in parallel naturally. So the computational complexity can be effectively reduced which means better hyper-parameters can be obtained under the same amount of calculation. Experiments are done on real world power load data,where the proposed method outperforms the existing state-of-the-art algorithms,BO with limit-BFGS-bound (BO-L-BFGS-B) and BO with truncated-newton (BO-TNC),in terms of the prediction accuracy. The errors of the prediction result in different models show that BO-PSO is an effective hyper-parameter optimization method.

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“…Structural health monitoring is an effective way to evaluate the safety and durability of a structure during its service life [25]. Similar to condition monitoring [26] and process monitoring [27,28], structural monitoring for spatial structures is to monitor the response [29,30] (displacement, stress, etc.) of the compression member or node in the weak area in which the members are apt to buckling, and to assess the stress state for reducing the probability of sudden destruction.…”

Section: Introductionmentioning

confidence: 99%

Static Behaviors and Applications of Buckling Monitoring Members with Rigid Ends

2021

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The buckling of compression members may lead to the progressive collapse of spatial structures. Based on the sleeved compression member, the buckling monitoring member is introduced to control the buckling of compression member and raise buckling alert by sensing contact between the core tube and the restraining tube. Considering the rigid connection among the members in spatial structures, the buckling monitoring member with rigid ends needs to be further analyzed. An experimental test was conducted and finite element analyses were performed with calibrated finite element models. The results indicated that the ultimate bearing capacity and post-ultimate bearing capacity of the core tube were enhanced due to the restraint from the restraining tube. The contact was successfully sensed by pressure sensor, revealing that it sensed the buckling of the core tube. Parametric studies were conducted, indicating that the core protrusion, core slenderness ratio, the gap between the core tube and restraining tube, and the flexural rigidity ratio are the key parameters affecting the bearing capacity and the failure modes of the buckling monitoring member, and some key values of parameters were proposed to obtain good bearing capacity. Based on the parametric studies, the failure modes of buckling-monitoring members are summarized as global buckling and local buckling. The stress distribution and deformation mode of buckling monitoring members are presented in the non-contact, point-contact, line-contact, reverse-contact and ultimate bearing state. The buckling monitoring member is applied in a reticulated shell by substituting the buckling members. It can effectively improve the ultimate bearing capacity of reticulated shell.

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A Review on Fault Detection and Process Diagnostics in Industrial Processes

Cited by 162 publications

References 154 publications

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

A New Hyper-Parameter Optimization Method for Power Load Forecast Based on Recurrent Neural Networks

Static Behaviors and Applications of Buckling Monitoring Members with Rigid Ends

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